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Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 25 December 2024 | Viewed by 7998

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Special Issue Editors

Dr. Yanni Dong

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Guest Editor

Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: hyperspectral target detection; dimensionality reduction; scene classification; metric learning; transfer learning; multi-source remote sensing data geological interpretation
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaochen Yang

E-Mail Website
Guest Editor

School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
Interests: distance metric learning; few-shot learning; hyperspectral image analysis; statistical classification
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qian Du

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Guest Editor

Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing image includes a rich description of the earth’s surface in various modalities (hyperspectral data, high resolution data, multispectral data, synthetic aperture radar (SAR) data, etc.). Remote sensing target detection or object detection aims to determine whether there are targets or objects of interest in the image, playing a decisive role in resource detection, environmental monitoring, urban planning, national security, agriculture, forestry, climate, hydrolog, etc. In recent years, artificial intelligence (AI) has achieved considerable development and been successfully applied for various applications, such as regression, clustering, classification, etc. Although AI-driven approaches can handle the massive quantities of data acquired by remote sensors, they require many high-quality labeled samples to deal with remote sensing big data, leading to fragile results. That is, AI-driven approaches with strong feature extraction abilities have limited performance and are still far from practical demands. Thus, target detection or object detection in the presence of complicated backgrounds with limited labeled samples remains a challenging mission. There is still much room for research on remote sensing target detection and object detection. The main goal of this Special Issue is to address advanced topics related to remote sensing target detection and object detection.

Topics of interests include but are not limited to the following:

New AI-driven methods for remote sensing data, such as GNN, transformer, etc.;
New remote sensing datasets, including hyperspectral, high resolution, SAR datasets, etc.;
Machine learning techniques for remote sensing applications, such as domain adaptation, few-shot learning, manifold learning, and metric learning;
Machine learning-based drone detection and fine-grained detection;
Target detection, object detection, and anomaly detection;
Data-driven applications in remote sensing;
Technique reviews on related topics.

Dr. Yanni Dong
Dr. Xiaochen Yang
Prof. Dr. Qian Du
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
target detection
artificial intelligence
machine learning
deep learning
object detection
new datasets

Related Special Issue

Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection in Remote Sensing (29 articles)

Published Papers (8 papers)

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Research

22 pages, 30157 KiB

Open AccessArticle

DaliWS: A High-Resolution Dataset with Precise Annotations for Water Segmentation in Synthetic Aperture Radar Images

by Shanshan Zhang, Weibin Li, Rongfang Wang, Chenbin Liang, Xihui Feng and Yanhua Hu

Remote Sens. 2024, 16(4), 720; https://doi.org/10.3390/rs16040720 - 18 Feb 2024

Viewed by 805

Abstract

The frequent occurrence of global flood disasters leads to millions of people falling into poverty each year, which poses immense pressure on governments and hinders social development. Therefore, providing more data support for flood disaster detection is of paramount importance. To facilitate the development of water body detection algorithms, we create the DaliWS dataset for water segmentation, which contains abundant pixel-level annotations, and consists of high spatial resolution SAR images collected from the GaoFen-3 (GF-3) satellite. For comprehensive analysis, extensive experiments are conducted on the DaliWS dataset to explore the performance of the state-of-the-art segmentation models, including FCN, SegNeXt, U-Net, and DeeplabV3+, and investigate the impact of different polarization modes on water segmentation. Additionally, to probe the generalization of our dataset, we further evaluate the models trained with the DaliWS dataset, on publicly available water segmentation datasets. Through detailed analysis of the experimental results, we establish a valuable benchmark and provide usage guidelines for future researchers working with the DaliWS dataset. The experimental results demonstrate the F1 scores of FCN, SegNeXt, U-Net, and DeeplabV3+ on the dual-polarization data of DaliWS dataset reach to 90.361%, 90.192%, 92.110%, and 91.199%, respectively, and these four models trained using the DaliWS dataset exhibit excellent generalization performance on the public dataset, which further confirms the research value of our dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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22 pages, 4810 KiB

Open AccessArticle

Ship Target Detection in Optical Remote Sensing Images Based on E2YOLOX-VFL

by Qichang Zhao, Yiquan Wu and Yubin Yuan

Remote Sens. 2024, 16(2), 340; https://doi.org/10.3390/rs16020340 - 15 Jan 2024

Viewed by 854

Abstract

In this research, E2YOLOX-VFL is proposed as a novel approach to address the challenges of optical image multi-scale ship detection and recognition in complex maritime and land backgrounds. Firstly, the typical anchor-free network YOLOX is utilized as the baseline network for ship detection. Secondly, the Efficient Channel Attention module is incorporated into the YOLOX Backbone network to enhance the model’s capability to extract information from objects of different scales, such as large, medium, and small, thus improving ship detection performance in complex backgrounds. Thirdly, we propose the Efficient Force-IoU (EFIoU) Loss function as a replacement for the Intersection over Union (IoU) Loss, addressing the issue whereby IoU Loss only considers the intersection and union between the ground truth boxes and the predicted boxes, without taking into account the size and position of targets. This also considers the disadvantageous effects of low-quality samples, resulting in inaccuracies in measuring target similarity, and improves the regression performance of the algorithm. Fourthly, the confidence loss function is improved. Specifically, Varifocal Loss is employed instead of CE Loss, effectively handling the positive and negative sample imbalance, challenging samples, and class imbalance, enhancing the overall detection performance of the model. Then, we propose Balanced Gaussian NMS (BG-NMS) to solve the problem of missed detection caused by the occlusion of dense targets. Finally, the E2YOLOX-VFL algorithm is tested on the HRSC2016 dataset, achieving a 9.28% improvement in mAP compared to the baseline YOLOX algorithm. Moreover, the detection performance using BG-NMS is also analyzed, and the experimental results validate the effectiveness of the E2YOLOX-VFL algorithm. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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22 pages, 1779 KiB

Open AccessArticle

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

by Fengxia Han, Jin Zeng, Le Zheng, Hongming Zhang and Jianhui Wang

Remote Sens. 2024, 16(2), 247; https://doi.org/10.3390/rs16020247 - 08 Jan 2024

Cited by 1 | Viewed by 744

Abstract

Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pilot overhead as well as substantial computational complexity. With benefits from the powerful detection characteristics of MIMO radar, we aim for designing a novel sensing-assisted semi-blind detection scheme in this paper, where both the inherent low-rankness of signal matrix and the essential knowledge about geometric environments are fully exploited under a designated cooperative manner. Specifically, to efficiently recover the channel factorizations via the formulated low-rank matrix completion problem, a low-complexity iterative algorithm stemming from the alternating steepest descent (ASD) method is adopted to obtain the solutions in case of unknown noise statistics. Moreover, we take one step forward by employing the denoising convolutional neural network (DnCNN) to preprocess the received signals due to its favorable performance of handling Gaussian denoising. The overall paradigm of our proposed scheme consists of three stages, namely (1) target parameter sensing, (2) communication signal denoising and (3) semi-blind detection refinement. Simulation results show that significant estimation gains can be achieved by the proposed scheme with reduced training overhead in a variety of system settings. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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Graphical abstract

18 pages, 3000 KiB

Open AccessArticle

A Lightweight Man-Overboard Detection and Tracking Model Using Aerial Images for Maritime Search and Rescue

by Yijian Zhang, Qianyi Tao and Yong Yin

Remote Sens. 2024, 16(1), 165; https://doi.org/10.3390/rs16010165 - 30 Dec 2023

Cited by 1 | Viewed by 1161

Abstract

Unmanned rescue systems have become an efficient means of executing maritime search and rescue operations, ensuring the safety of rescue personnel. Unmanned aerial vehicles (UAVs), due to their agility and portability, are well-suited for these missions. In this context, we introduce a lightweight detection model, YOLOv7-FSB, and its integration with ByteTrack for real-time detection and tracking of individuals in maritime distress situations. YOLOv7-FSB is our lightweight detection model, designed to optimize the use of computational resources on UAVs. It comprises several key components: FSNet serves as the backbone network, reducing redundant computations and memory access to enhance the overall efficiency. The SP-ELAN module is introduced to ensure operational speed while improving feature extraction capabilities. We have also enhanced the feature pyramid structure, making it highly effective for locating individuals in distress within aerial images captured by UAVs. By integrating this lightweight model with ByteTrack, we have created a system that improves detection accuracy from 86.9% to 89.2% while maintaining a detection speed similar to YOLOv7-tiny. Additionally, our approach achieves a MOTA of 85.5% and a tracking speed of 82.7 frames per second, meeting the demanding requirements of maritime search and rescue missions. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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19 pages, 29513 KiB

Open AccessArticle

A Multi-Stream Attention-Aware Convolutional Neural Network: Monitoring of Sand and Dust Storms from Ordinary Urban Surveillance Cameras

by Xing Wang, Zhengwei Yang, Huihui Feng, Jiuwei Zhao, Shuaiyi Shi and Lu Cheng

Remote Sens. 2023, 15(21), 5227; https://doi.org/10.3390/rs15215227 - 03 Nov 2023

Cited by 1 | Viewed by 639

Abstract

Sand and dust storm (SDS) weather has caused several severe hazards in many regions worldwide, e.g., environmental pollution, traffic disruptions, and human casualties. Widespread surveillance cameras show great potential for high spatiotemporal resolution SDS observation. This study explores the possibility of employing the surveillance camera as an alternative SDS monitor. Based on SDS image feature analysis, a Multi-Stream Attention-aware Convolutional Neural Network (MA-CNN), which learns SDS image features at different scales through a multi-stream structure and employs an attention mechanism to enhance the detection performance, is constructed for an accurate SDS observation task. Moreover, a dataset with 13,216 images was built to train and test the MA-CNN. Eighteen algorithms, including nine well-known deep learning models and their variants built on an attention mechanism, were used for comparison. The experimental results showed that the MA-CNN achieved an accuracy performance of 0.857 on the training dataset, while this value changed to 0.945, 0.919, and 0.953 in three different real-world scenarios, which is the optimal performance among the compared algorithms. Therefore, surveillance camera-based monitors can effectively observe the occurrence of SDS disasters and provide valuable supplements to existing SDS observation networks. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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19 pages, 8534 KiB

Open AccessArticle

An Infrared Maritime Small Target Detection Algorithm Based on Semantic, Detail, and Edge Multidimensional Information Fusion

by Jiping Yao, Shanzhu Xiao, Qiuqun Deng, Gongjian Wen, Huamin Tao and Jinming Du

Remote Sens. 2023, 15(20), 4909; https://doi.org/10.3390/rs15204909 - 11 Oct 2023

Cited by 1 | Viewed by 994

Abstract

The infrared small target detection technology has a wide range of applications in maritime defense warning and maritime border reconnaissance, especially in the maritime and sky scenes for detecting potential terrorist attacks and monitoring maritime borders. However, due to the weak nature of infrared targets and the presence of background interferences such as wave reflections and islands in maritime scenes, targets are easily submerged in the background, making small infrared targets hard to detect. We propose the multidimensional information fusion network(MIFNet) that can learn more information from limited data and achieve more accurate target segmentation. The multidimensional information fusion module calculates semantic information through the attention mechanism and fuses it with detailed information and edge information, enabling the network to achieve more accurate target position detection and avoid detecting one target as multiple ones, especially in high-precision scenes such as maritime target detection, thus effectively improving the accuracy and reliability of detection. Moreover, experiments on our constructed dataset for small infrared targets in maritime scenes demonstrate that our algorithm has advantages over other state-of-the-art algorithms, with an IoU of 79.09%, nIoU of 79.43%, F1 score of 87.88%, and AuC of 95.96%. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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20 pages, 35416 KiB

Open AccessArticle

Hybrid Task Cascade-Based Building Extraction Method in Remote Sensing Imagery

by Runqin Deng, Meng Zhou, Yinni Huang and Wei Tu

Remote Sens. 2023, 15(20), 4907; https://doi.org/10.3390/rs15204907 - 11 Oct 2023

Viewed by 904

Abstract

Instance segmentation has been widely applied in building extraction from remote sensing imagery in recent years, and accurate instance segmentation results are crucial for urban planning, construction and management. However, existing methods for building instance segmentation (BSI) still have room for improvement. To achieve better detection accuracy and superior performance, we introduce a Hybrid Task Cascade (HTC)-based building extraction method, which is more tailored to the characteristics of buildings. As opposed to a cascaded improvement that performs the bounding box and mask branch refinement separately, HTC intertwines them in a joint multilevel process. The experimental results also validate its effectiveness. Our approach achieves better detection accuracy compared to mainstream instance segmentation methods on three different building datasets, yielding outcomes that are more in line with the distinctive characteristics of buildings. Furthermore, we evaluate the effectiveness of each module of the HTC for building extraction and analyze the impact of the detection threshold on the model’s detection accuracy. Finally, we investigate the generalization ability of the proposed model. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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18 pages, 7073 KiB

Open AccessArticle

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

by Masoud Alajmi, Hayam Alamro, Fuad Al-Mutiri, Mohammed Aljebreen, Kamal M. Othman and Ahmed Sayed

Remote Sens. 2023, 15(18), 4600; https://doi.org/10.3390/rs15184600 - 19 Sep 2023

Cited by 1 | Viewed by 988

Abstract

Remote sensing imagery involves capturing and examining details about the Earth’s surface from a distance, often using satellites, drones, or other aerial platforms. It offers useful data with which to monitor and understand different phenomena on Earth. Vehicle detection and classification play a crucial role in various applications, including traffic monitoring, urban planning, and environmental analysis. Deep learning, specifically convolutional neural networks (CNNs), has revolutionized vehicle detection in remote sensing. This study designs an improved Chimp optimization algorithm with a DL-based vehicle detection and classification (ICOA-DLVDC) technique on RSI. The presented ICOA-DLVDC technique involves two phases: object detection and classification. For vehicle detection, the ICOA-DLVDC technique applies the EfficientDet model. Next, the detected objects can be classified by using the sparse autoencoder (SAE) model. To optimize the SAE’s hyperparameters effectively, we introduce an ICOA which streamlines the parameter tuning process, accelerating convergence and enhancing the overall performance of the SAE classifier. An extensive set of experiments has been conducted to highlight the improved vehicle classification outcomes of the ICOA-DLVDC technique. The simulation values demonstrated the remarkable performance of the ICOA-DLVDC approach compared to other recent techniques, with a maximum accuracy of 99.70% and 99.50% on the VEDAI dataset and ISPRS Postdam dataset, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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